Evidence map›Paper›PMID 41246211›Full record

ArticleDigital health

Evaluating empathy in GPT-4-generated vs. physician-written emergency department discharge letters.

Gal Ben-Haim, Adva Livne, Uri Manor, David Hochstein, Mor Saban, Orly Blaier, Yael Abramov Iram, Moran Gigi Balzam, Ariel Lutenberg, Rowand Eyade and 10 more

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Gal Ben-HaimDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.ORCID https://orcid.org/0000-0002-0532-2864
Adva LivneFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.ORCID https://orcid.org/0009-0008-4674-9937
Uri ManorFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.ORCID https://orcid.org/0000-0003-3912-6957
David HochsteinFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.ORCID https://orcid.org/0000-0001-6388-4278
Mor SabanFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Orly BlaierDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Yael Abramov IramDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Moran Gigi BalzamDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Ariel LutenbergDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Rowand EyadeDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Roula QassemDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Dan TrabelsiFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Yarden DahariFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Ben Zion EisenmannDepartment of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Yelena ShechtmanBnei-Zion Medical Center, Haifa, Israel.
Girish N NadkarniThe Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Benjamin S GlicksbergThe Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Eyal ZimlichmanFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Anat PerryPsychology Department, The Hebrew University of Jerusalem, Jerusalem, Israel.
Eyal KlangThe Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Empathy improves clinical outcomes, patient satisfaction, and adherence to treatment. Few studies have explored the real-world use of large language models in conveying empathy. We compared the empathy in emergency department (ED) discharge letters written by GPT-4 and physicians. Methods: We conducted a retrospective, blinded, comparative study in a tertiary ED. All patients discharged for one 8-h shift were included. For each patient, we compared the original ED discharge letter to a GPT-4 generated letter. GPT-4 generated the letters using ED notes, excluding the original discharge letter. Seventeen evaluators (seven physicians, five nurses, five patients) compared the letters side by side. They were blinded to the source. Evaluators first chose between the AI and human letters. Then they rated each letter for empathy, overall quality, clarity of summary, and clarity of recommendations using a 5-point Likert scale. Results: Evaluators preferred GPT-4 over physician letters in 83.7% of comparisons (1009 vs. 197; Conclusion: GPT-4 shows strong potential in generating empathetic ED discharge letters. These letters are preferred by healthcare professionals and patients. GPT-4 offers a promising tool to reduce the workload of ED physicians. Further research is necessary to explore patient perceptions and best practices for integrating AI with physicians in clinical practice.

Indexed as

claritydischarge lettersemergency departmentEmpathyGPT-4

Identifiers

PMID41246211
PMCPMC12615915

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.